BMB831: Biostatistics in R II (5 ECTS)

STADS: 01014401

Master's level course

Teaching period
The course is offered in the autumn semester.

Teacher responsible
Veit Schwämmle, Lektor, Ph.d.
Tlf.: 6550 9999 Email:

Group Type Day Time Classroom Weeks Comment
Common I Monday 10-12 U72 47
Common I Tuesday 14-16 U24 45
Common I Wednesday 10-12 U49B 45
Common I Thursday 10-12 U148 44
Common I Thursday 10-12 U24 45
Common I Thursday 14-16 U24 48
Common I Thursday 12-14 U72 50
H1 TE Monday 10-12 U155 46
H1 TE Monday 10-12 U142 48-49
H1 TE Monday 14-16 U154 51
H1 TE Tuesday 14-16 U72 47
H1 TE Tuesday 10-12 U17 48
H1 TE Tuesday 10-12 U146 50
H1 TE Wednesday 12-14 U147 46
H1 TE Wednesday 14-16 U72 47
H1 TE Wednesday 12-14 U21 49
H1 TE Wednesday 12-14 U146 51
H1 TE Thursday 12-14 U146 51
H1 TE Friday 10-12 U148 44
H1 TE Friday 08-10 U30 45
H1 TE Friday 14-16 U72 46
H1 TE Friday 10-12 U142 49
H1 TE Friday 09-11 U72 50
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Academic preconditions:
Students taking the course are expected to:
  • Have knowledge in statistics 
  • Understand the basic principles of molecular biology
  • Have basic programming skills in R
  • Know the fundamentals of biostatistics

Course introduction
Modern experimental platforms nowadays deliver the quantification of pools of biological molecules. Their analysis requires complex bioinformatics pipelines to obtain biologically relevant results. The students will use the acquired knowledge to design and apply work flows that handle omics data sets. The course consists of a theoretical and an extensive practical part, with the objective to provide advanced understanding of data analysis with R scripts and application of bioinformatics tools.

The course will introduce the students to advanced programming of R scripts necessary to deal with data from modern high-throughput experiments and gives a broad overview of tools for biological interpretation. Exercises involve in-depth application of standard pipelines to process omics data and a final project to apply the acquired abilities on real data that might come from experiments previously carried out by the student, e.g. during their bachelor/master thesis.

Expected learning outcome
The learning objectives of the course are that the student demonstrates the ability to:
  • independently analyze even conceptually demanding data sets. 
  • work with large data amounts and carry out standard statistical analysis to identify relevant features. 
  • use standard algorithms for multi-variate analysis
  • design scripts for detailed visualization of their results. 
  • know and apply tools for data interpretation.
  • know and apply standard pipelines for the processing of omics data.
  • know how to objectively discuss applied data analysis methods presented e.g. in publications.
Subject overview
The following main topics are contained in the course:
  • statistics for large data sets
  • different types of data modeling
  • advanced data visualization
  • advanced data interpretation
  • computational tools for protein characterization
  • standard work flows for data from omics experiments
    Der er i øjeblikket ikke angivet nogle materialer for kurset.

This course uses e-learn (blackboard).

Prerequisites for participating in the exam
  1. Tutorial and exercises. Pass/fail, internal marking by teacher. The prerequisite examination is a prerequisite for participation in exam element a). (01014412)
Assessment and marking:
  1. Individual report. External marking, Danish 7-mark scale (01014402)

Allowed exam aids: Blackboard / Whiteboard.

Expected working hours
The teaching method is based on three phase model.
Intro phase: 16 hours
Skills training phase: 30 hours, hereof:
 - Tutorials: 30 hours

Educational activities

Educational form
In the individual report, the student describes how they solve a given biostatistics assignment. Details on the individual assignment will be discussed during the exercises. The report should consist of at least 5 pages with a maximum of  10 pages excluding references. It should contain the following sections: a) Introduction and description of assignment; b) Overview of the student's procedure to solve the task; and c) evaluation of results and conclusion.

This course is taught in English.

Course enrollment
See deadline of enrolment.

Tuition fees for single courses
See fees for single courses.